First-trimester metabolomic prediction of stillbirth

Background: Stillbirth remains a major problem in both developing and developed countries. Omics evaluation of stillbirth has been highlighted as a top research priority. Objective: To identify new putative first-trimester biomarkers in maternal serum for stillbirth prediction using metabolomics-based approach. Methods: Targeted, nuclear magnetic resonance (NMR) and mass spectrometry (MS), and untargeted liquid chromatography-MS (LC-MS) metabolomic analyses were performed on first-trimester maternal serum obtained from 60 cases that subsequently had a stillbirth and 120 matched controls. Metabolites by themselves or in combination with clinical factors were used to develop logistic regression models for stillbirth prediction. Prediction of stillbirths overall, early (<28 weeks and <32 weeks), those related to growth restriction/placental disorder, and unexplained stillbirths were evaluated. Results: Targeted metabolites including glycine, acetic acid, L-carnitine, creatine, lysoPCaC18:1, PCaeC34:3, and PCaeC44:4 predicted stillbirth overall with an area under the curve [AUC, 95% confidence interval (CI)] = 0.707 (0.628–0.785). When combined with clinical predictors the AUC value increased to 0.740 (0.667–0.812). First-trimester targeted metabolites also significantly predicted early, unexplained, and placental-related stillbirths. Untargeted LC-MS features combined with other clinical predictors achieved an AUC (95%CI) = 0.860 (0.793–0.927) for the prediction of stillbirths overall. We found novel preliminary evidence that, verruculotoxin, a toxin produced by common household molds, might be linked to stillbirth. Conclusions: We have identified novel biomarkers for stillbirth using metabolomics and demonstrated the feasibility of first-trimester prediction.

研究背景：死产（stillbirth）仍是发展中国家与发达国家共同面临的重大公共卫生问题，死产的组学（Omics）评估已被列为首要研究重点。
研究目的：采用基于代谢组学（metabolomics）的研究方法，在孕早期母体血清中筛选可用于死产预测的潜在新型生物标志物。
研究方法：针对60例后续发生死产的受试者及120例匹配对照的孕早期母体血清样本，分别开展靶向代谢组学分析（结合核磁共振（NMR）与质谱（MS）技术）以及非靶向液相色谱-质谱（LC-MS）代谢组学分析。以单一代谢物或代谢物联合临床因素构建逻辑回归模型，用于死产预测；同时评估模型对总体死产、早期死产（孕28周前及孕32周前）、生长受限/胎盘疾病相关死产以及不明原因死产的预测性能。
研究结果：针对甘氨酸、乙酸、左旋肉碱、肌酸、lysoPCaC18:1、PCaeC34:3及PCaeC44:4等靶向代谢物的分析可预测总体死产，其曲线下面积（area under the curve, AUC，95%置信区间（confidence interval, CI））为0.707（0.628~0.785）；当联合临床预测因素时，AUC值提升至0.740（0.667~0.812）。孕早期靶向代谢物同样可显著预测早期死产、不明原因死产及胎盘相关死产。非靶向LC-MS特征联合其他临床预测因素用于总体死产预测时，AUC（95%CI）可达0.860（0.793~0.927）。本研究发现新的初步证据表明，常见家用霉菌产生的毒素疣孢菌素（verruculotoxin）可能与死产存在关联。
研究结论：本研究通过代谢组学技术筛选出死产相关的新型生物标志物，并证实了孕早期死产预测的可行性。